The present disclosure relates to the field of service quality management, and more particularly to increasing the accuracy of service quality management metrics. Deployment of data services require an industrializing of the carrier's data program and having user experience influence network operations. Service quality management (“SQM”) entails the monitoring and maintenance of end-to-end services for specific customers or classes of customers. Service quality is the collective effect of service performances which determine the degree of satisfaction of a user of the service. A service is a means of delivering value to customers by facilitating outcomes customers/end-users want to achieve but without the ownership of specific costs and risks. SQM allows one to ascertain how a service is performing and how that service is experienced by the user.
FIG. 1 depicts a typical environment for SQM that comprises data sources, a server, and one or more clients. The server has a key quality indicator (“KQI”) combination layer and a service level agreement (“SLA”) layer. The KQI combination layer is where the KQIs are created and the SLA layer is where the SLA is modeled. The service quality data store is where the computed KQIs and received key performance indicators (“KPIs”) are further stored for reporting. Here, granular metrics (“data sources”) are collected for each of the service components that are then used to make a higher-level metric.
The mapping of higher-level metrics to granular metrics is hard-coded; hence, when downstream components are not functioning properly there are gaps in the service quality measurement. Monitoring business services requires the collection of different metrics from a variety of monitoring systems, such as network topology, fault, performance, configuration, and more. Typically, each set of metrics are defined and collected by separate and often isolated, assurance systems. The metrics collected by each assurance system are pre-defined at deployment time.
The metric data is aggregated to determine the overall health of a business service, according to pre-defined service key performance indicators (“KPIs”), which is a type of performance measurement that measure a specific aspect of the performance of either a service resource or a group of service resources of the same type. KPIs are typically restricted to a specific resource type and derived from network measurements. If an assurance system that is providing metrics to the business service management (“BSM”) system is not functioning, there is a gap in metrics obtained, and therefore the business service is deemed to be compromised or degraded.
The current nature of business service management is pre-defined and static, relying on pre-defined metrics being collected by various assurance and resource monitoring systems. If an assurance system is unavailable, this often results in a window of missing data over a period of time, leading to the business service being depicted as in poor health or degraded according to it's KPIs. Current solutions today utilize “self-mirroring” or manage-our-stuff-with-our-stuff, which sends a synthetic alert when one of the monitoring systems that generate service quality data is down. Upon receiving such an alert, the operations staff may take corrective action; however, the service assurance data will still be missing for the time period of the outage.